Generally, the central problem in parameter estimation is the following [1]}, [2]} : For a data set \(x\)  of \(n\)  test results, how accurately can we predict the value of \(\theta \)  ? Indeed, in physics, the parameter \(\theta \)  is a physical property of the system studied and we want to determine its value from the experimental data \(x_{i}\) . For this purpose, an estimator is used to transform the data \(x\)  into a prediction of \(\theta \) . We will denote such an estimator by \(\hat{\theta }\left(x \right)\) , which happens to be a random variable since it depends on the values taken from the random variables in \(X\) . In other words, estimating the parameter \(\theta \)  consists in giving an approximate value to this parameter from a population survey. This implies that the true value of \(\theta \)  is obtained if \(\theta \)  coincides with the mathematical expectation of its estimator \(\hat{\theta }\left(X\right)\) , i.e. \(\theta =\textbf {E}\left[\hat{\theta }\left(X \right)\right]\) .
